Scalable network MIMO for wireless networks

ABSTRACT

Systems and methods for system for channel access adaptation are disclosed. One system includes a plurality of remote antennas and a plurality of access points. The remote antennas transmit data to receivers and obtain channel state information. Additionally, each access point controls a different cluster of the remote antennas and receives the respective channel state information from the remote antennas of the cluster. Further, each access point is configured to, independently from other access points, adapt channel allocations to the remote antennas of the respective cluster based on a tracking of sums of collision loss probabilities. Each given sum is determined by the access point for a different given set of a plurality of sets of cooperating remote antennas in the respective cluster, where each constituent collision loss probability in the given sum is determined by the access point from a different interference clique to which the given set belongs.

RELATED APPLICATION INFORMATION

This application claims priority to provisional application Ser. No.61/608,381 filed on Mar. 8, 2012, incorporated herein by reference.

BACKGROUND

1. Technical Field

The present invention relates to wireless communication methods andsystems and, more particularly, to network multiple input multipleoutput (MIMO) systems and methods.

2. Description of the Related Art

Network MIMO (netMIMO) is a family of techniques that has the potentialto enhance the capacity of wireless systems via tight coordinationbetween access points (APs), thus enabling them to serve multiple usersconcurrently. Numerous existing schemes, traditionally used inmulti-user MIMO systems, have been applied to establish netMIMO byintegrating distributed APs into one giant-MIMO. Several works havelooked into the design of efficient multi-user MIMO (single cell andnetMIMO multiple cells/links) transmission schemes in the physical layerthat have been adopted in next generation standards, such as 802.11 acand CoMP (Coordinated Multi-Point) in LTE (Long Term Evolution). Therehave also been several practical realizations of such MU-MIMO andnetMIMO schemes in the networking community that showcase theirreal-world performance. Existing works have focused on the realizationof the netMIMO scheme itself and hence only considered small scaletopologies where channel state information is shared amongst alltransmitters.

SUMMARY

One embodiment of the present principles is directed to a system forchannel access adaptation. The system includes a plurality of remoteantennas and a plurality of access points. The remote antennas areconfigured to transmit data to receivers and to obtain channel stateinformation. In addition, each of the access points is configured tocontrol a different cluster of the remote antennas and receive therespective channel state information from the remote antennas of therespective cluster. Further, each of the access points is configured to,independently from other access points, adapt channel allocations to theremote antennas of the respective cluster based on a tracking of sums ofcollision loss probabilities. Each given sum is determined by thecorresponding access point for a different given set of a plurality ofsets of cooperating remote antennas in the respective cluster, whereeach constituent collision loss probability in the given sum isdetermined by the corresponding access point from a differentcorresponding interference clique to which the given set belongs.

Another embodiment is also directed to a system for channel accessadaptation. The system comprises a cluster of remote antennas and anaccess point. The remote antennas are configured to transmit data toreceivers and to obtain channel state information. Further, the accesspoint is configured to receive the channel state information from theremote antennas and adapt channel allocations to the remote antennasbased on a tracking of sums of collision loss probabilities. Each givensum is determined by the access point for a different given set of aplurality of sets of cooperating remote antennas in the cluster, whereeach constituent collision loss probability in the given sum isdetermined by the access point from a different correspondinginterference clique to which the given set belongs.

Another embodiment is directed to a method for channel accessadaptation. In accordance with the method, channel state information isreceived from a cluster of remote antennas. Further, channel allocationsfor the remote antennas of the cluster are determined based on atracking of sums of collision loss probabilities. Each given sum isdetermined for a different given set of a plurality of sets ofcooperating remote antennas in the cluster, where each constituentcollision loss probability in the given sum is determined from adifferent corresponding interference clique to which the given setbelongs. In addition, data from at least one of said sets of cooperatingremote antennas in the cluster is transmitted in accordance with thechannel allocations.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF DRAWINGS

The disclosure will provide details in the following description ofpreferred embodiments with reference to the following figures wherein:

FIG. 1 is a high-level block diagram of a network MIMO system inaccordance with an exemplary embodiment;

FIG. 2 is a high-level block/flow diagram of various components of anetwork MIMO system in accordance with an exemplary embodiment;

FIG. 3 is a high-level block/flow diagram of a method for channel accessadaptation in accordance with an exemplary embodiment; and

FIG. 4 is a state diagram for a given set of cooperating antennasillustrating an adaptation for the set.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

Several existing network MIMO schemes are limited to small-scalenetworks due to a stringent synchronization requirement and the overheadin sharing data signals between APs. A straight-forward approach ofdirectly porting these solutions to large networks would not be feasible(scalable) in terms of synchronization and overhead. Further, lack ofdata sharing would also limit performance scalability. On the otherhand, restricting netMIMO to a small set of transmitters, albeitfeasible, would compromise the netMIMO gains due to interference fromnon-cooperating transmitters. Achieving scalability in terms of bothperformance and feasibility, in turn, involves striking an efficientbalance between these conflicting objectives.

The notion of addressing interference in wireless networks has changedconsiderably with the advent of MIMO. While interference wastraditionally resolved through collision avoidance mechanisms initially,as in, for example, carrier sense multiple access schemes (CSMA), thespatial degrees of freedom (DoFs) in MIMO permit for interference to notjust be suppressed but also exploited. Interference between links thatwas traditionally resolved through collision-avoidance mechanisms cannow be suppressed or even exploited in MIMO by enabling the interferinglinks to cooperate and share interference information, including channelstate information and data. Such mechanisms are often collectivelyreferred to as network MIMO, multi-user MIMO (MU-MIMO), and CoMPtransmissions.

Sharing channel state information alone can only help suppressinterference between links, whereby the number of concurrenttransmissions will scale only with the number of antennas on a link.However, sharing of data as well will enable interference to beexploited and converted into a cooperation gain, for example receptionat a receiver from multiple transmitters, thereby permitting performanceto scale with the number of transmitters. The benefits of network MIMOare realized at the cost of interference information exchange, forexample, on the backhaul, as well as synchronization between thecooperating transmitters, as tighter phase synchronization is needed fordata sharing.

Exemplary embodiments described herein below employ NEMOx, a novelarchitecture that realizes the netMIMO gain in large-scale wirelessnetworks. NEMOx organizes a network into multiple clusters, eachincluding one AP and multiple cooperating points (CPs)—each being aremote antenna—that are fully synchronized at the AP. NEMOx managesinter-cluster interference using a decentralized channel-accessalgorithm that opportunistically groups the CPs for cooperation, whileleaving sufficient room for spatial reuse between clusters. Within eachcluster, NEMOx optimizes the set of clients to serve, and the powerallocation from CPs to the clients, in order to maximize the netMIMOgain while ensuring fairness among them. It can be shown that NEMOxdelivers multiple folds of capacity gain compared to distributednetworks without MIMO cooperation. It can also be shown that NEMOxincurs limited inter-cluster contention overhead and that its capacityscales well with the number of clusters and with the number of CPswithin each cluster.

Exemplary embodiments described herein provide a framework that enablesan efficient partition of the transmission points into several clusterswhere (i) inside each cluster the transmission strategy is fullysynchronized and there is a central decision making process for thephysical layer and media access control (MAC) layer problems such asbeamforming, precoding, power control and scheduling, while (ii) betweenthe clusters there is no coordination and the transmission strategy isfully decentralized. Further, the embodiments have relatively lowoverhead. For example, local information is exchanged within a clusterand hence has lower overhead and cost associated with it in comparisonto a full scale version of NetMIMO proposals. Moreover, the embodimentsdescribed herein can easily scale with an increase in the number ofremote antennas or transmission points. In addition, the embodimentsprovide an ease of synchronization. For example, since all thetransmission points within a cluster are in fact connected directly to acentral access point or base station they can share the same clock andbe fully synchronized (up to a symbol level). Although the problem ofMedium Access Control is much harder to address with the use of multipletransmission points, the exemplary embodiments of the present principlesdescribed herein apply an intelligent MAC scheme that reduces theinterference caused to the neighboring clusters or cells while enhancingthe cooperation among the transmission points or antennas inside acluster. The exemplary MAC scheme described herein also consideredfairness among all transmission points irrespective of the clusters towhich they are assigned.

There are several important features of the exemplary embodimentsdescribed herein below. For example, the structure of the transmissionscheme uses a clustering approach with centralized control in eachcluster and decentralized control between different clusters. Also,remote antenna head ends can be used for each transmission point insteadof full access points to permit a simpler and low-cost deployment aswell as to make the synchronization readily possible between thetransmission points inside a cluster. The physical transmission strategycomprises precoding, power allocation and user selections. In accordancewith exemplary aspects of the present principles, a plurality oftransmission strategies can be used. For example, one strategy providesan optimal scheme that involves solving an optimization problem. Anotherstrategy employs simple precoder generation and intelligent powercontrol for a selected user which has low complexity and provides nearoptimal performance. Here, the AP uses an algorithm to assign the CPs inthe set to clients with downlink packets. The assignment problem caninvolve several coupled factors: (i) The number of clients should notexceed the number of CPs in the cooperating set; (ii) A client'sachievable rate depends on not just its own channel gain, but also peersin the same set; (iii) The power budget of each CP should be spentintelligently such that sufficient “cancellation power” is used toeliminate the inter-stream interference, and the remaining power ensuresefficient and fair rate allocation among clients. Furthermore, the MACdesign described herein below enables independent and decentralizedchannel access adaptations within each cluster in a way that optimizesthe utility of the system.

It should be understood that embodiments described herein may beentirely hardware or may include both hardware and software elements,which includes but is not limited to firmware, resident software,microcode, etc. In a preferred embodiment, the present invention isimplemented in hardware and software.

Embodiments may include a computer program product accessible from acomputer-usable or computer-readable medium providing program code foruse by or in connection with a computer or any instruction executionsystem. A computer-usable or computer readable medium may include anyapparatus that stores, communicates, propagates, or transports theprogram for use by or in connection with the instruction executionsystem, apparatus, or device. The medium can be magnetic, optical,electronic, electromagnetic, infrared, or semiconductor system (orapparatus or device) or a propagation medium. The medium may include acomputer-readable storage medium such as a semiconductor or solid statememory, magnetic tape, a removable computer diskette, a random accessmemory (RAM), a read-only memory (ROM), a rigid magnetic disk and anoptical disk, etc.

A data processing system suitable for storing and/or executing programcode may include at least one processor coupled directly or indirectlyto memory elements through a system bus. The memory elements can includelocal memory employed during actual execution of the program code, bulkstorage, and cache memories which provide temporary storage of at leastsome program code to reduce the number of times code is retrieved frombulk storage during execution. Input/output or I/O devices (includingbut not limited to keyboards, displays, pointing devices, etc.) may becoupled to the system either directly or through intervening I/Ocontrollers.

Network adapters may also be coupled to the system to enable the dataprocessing system to become coupled to other data processing systems orremote printers or storage devices through intervening private or publicnetworks. Modems, cable modem and Ethernet cards are just a few of thecurrently available types of network adapters.

As indicated above, no coordination or synchronization between clustersis required for scalability in the exemplary embodiments describedherein. Thus, the channel-access mechanism should be asynchronous anddecentralized in nature. Random access mechanisms provide a light-weightapproach to achieving this goal. Although their asynchronous accesscannot provide any cooperation gain, they enable clusters to infer andreact to interference via avoidance, while permitting for spatialreuse—a key factor for scalable network-wide performance. The resolutionof contention (collision) in random access mechanisms are traditionallyaddressed through two approaches: persistence and backoff. While accessto the medium is controlled by a probabilistic parameter inpersistence-based access, it is the backoff window that isprobabilistically adapted in backoff-based access. In either case, thecontention parameter is adapted to ensure the desired efficiency andfairness properties. Since forward-engineering MAC protocols withprovable performance and fairness guarantees for backoff-based schemesare difficult to realize in practice, the exemplary embodimentsdescribed herein below are based on persistence.

In conventional persistence-based MAC schemes for omnidirectionalantenna networks, fairness and access (contention) are associated withan individual node (CP). In accordance with the exemplary embodimentsdescribed herein, a CP can cooperate with different sets of CPs (withina cluster). Hence, channel access and contention are coupled acrossmultiple CPs which may, unfortunately, have an inconsistent view of thechannel status. CPs can realize MIMO cooperation only when all of theirtransmission attempts are synchronized (i.e., they sense an idle channeland finish backoff at the same time). A straightforward approach is towait for the opportunity when all CPs are available. However, such astrict binding of CPs can severely hinder spatial reuse, and provideonly marginal gains over CSMA, although multiplexing gain is maximallyexploited. Alternatively, CPs may access the channel independently usinga conventional MAC scheme (e.g., CSMA) and opportunistically form groupswith each other when they are available. However, due to theasynchronous nature of channel access, such multiplexing opportunitiesare rare, thereby diminishing the gains from netMIMO.

Exemplary embodiments described herein meet the above-describedchallenge by grouping the CPs within each cluster into multiplecooperating sets. For example, referring now to the drawings in whichlike numerals represent the same or similar elements and initially toFIG. 1, an exemplary communication system 100 in which embodiments ofthe present principles can be implemented is illustratively depicted.The system 100 includes a plurality of access points 102, each of whichservices a different cluster 108 a and 108 m of CPs or remote antennas106 in respective systems 101 a and 101 m. Although two clusters 108 aand 108 m and two corresponding APs 102 are shown for illustrativepurposes, a particularly advantageous aspect of the principles describedherein is that the system 100 is scalable and can include many moreclusters of remote antennas and corresponding access points that controlthem. Each CP can transmit channel state information received fromreceivers 106 along a backhaul 110 and can in turn receive data fortransmission to the receiver(s) 106 it services from the AP 102 throughthe backhaul 110. Moreover, as discussed herein below, a receiver 106can receive the same data from a set 112 of cooperating remote antennas104 to implement cooperation gains that are achievable with NetMIMO. Asillustrated in FIG. 1, the system 100 can include a plurality ofcooperating sets 112.

FIG. 2 provides a more detailed depiction of the access points 102 andreceivers 106. The AP 102 can include a controller 204 that can beconfigured to perform channel access adaption in accordance with themethods described herein below. In accordance with one embodiment, thecontroller 204 can be configured to execute a program of instructionsstored on a storage medium 206 to implement the adaptation. The APsystem 102 can further include CP suppression and pruning modules 210and 212 respectively. Further, the system can comprise a transmissionmodule 213, which can include a client contention director module 214, aclient selection module 216, a bit to symbol mapper 218, a precoding andpower allocation module 220 and an Orthogonal Frequency-DivisionMultiplexing (OFDM) modulator 222. The transmission module 213 cancommunicate data to the remote antenna 104 for transmission to receivers106. The functions of the various elements of the AP 102 are describedin more detail herein below.

The receiver can include a receiver antenna 254 for receiving data fromremote antennas 104, a packet detection module 256, a channel estimationmodule 258, an OFDM demodulator 260 and a symbol to bit mapping module262. The channel estimation module 258 can be configured to obtaininterference measurements for channel resources and can determinechannel state information based on the interference measurements. Thechannel state information can be fed back to the controller 204 and theprecoding and power allocation module 220 through the receiver antenna254 and the remote antenna 104.

Referring again to FIG. 1, the cooperating sets 112 contend for channelaccess with each other, and with cooperating sets in neighboringclusters, and a winning set in a cluster permits all its CPs to transmitsynchronously. Channel-access attempts of each set are managed by adistributed network utility maximization (NUM) framework, whichoptimizes the efficiency while ensuring proportional fair access for allCPs.

Note that in network MIMO, a client can 106 jointly receive data frommultiple CPs. Hence, unlike in existing schemes, where fairness can bedefined with respect to a link (Tx-Rx pair) and hence a client, such anotion does not exist in network MIMO, where a client's rate depends notonly on its cooperating set of transmitters but also on the otherclients to whom the joint transmission is made. Hence, fairness inaccess is defined herein below with respect to each CP, while thefairness with respect to clients is addressed during the netMIMOoperation once channel access is obtained. Thus, we associate a utilityfunction U(r_(i)) for each CP, c_(i), where r_(i) is the rate receivedby it and U(•) is a non-negative, concave, differentiable function. Thechoice of the utility function determines the nature of fairness modelachieved. The MAC design can be forward-engineered through the followingoptimization.

${NCA}\text{:}\mspace{14mu}{Maximize}\mspace{14mu}{\sum\limits_{C_{m} \in C}{\sum\limits_{i \in C_{m}}{\alpha_{m}{U\left( r_{i} \right)}}}}$${{\sum\limits_{j:{S_{j} \in M_{k}}}q_{j}} \leq 1};{\forall{M_{k} \in \mathcal{M}}}$${{where}\mspace{14mu} r_{i}} = {\sum\limits_{j:{i \in S_{j}}}{\left( {1 - P_{j}} \right)q_{j}}}$where C denotes the set of clusters; α_(m) is the weight used forprioritizing cluster C_(m); q_(j) represents the channel-accessprobability for each cooperating set S_(j) whose collision (contentionwith sets in same and other clusters) loss probability is P_(j). Inomnidirectional communication, interference is captured through thenotion of maximal cliques, where all nodes in the clique collide witheach other. Equivalently in network MIMO, we define maximal cliques(M_(k)) with respect to cooperating sets (S_(j)), where a cooperatingset collides with another if at least one of the joint transmissions inthe other set suffers interference above a certain threshold. Forexample, the interference can be measured as a signal to interferenceplus noise ratio (SINR) such that if the SINR is below a certainthreshold, then the corresponding set is deemed part of the maximalclique. Note that one of the implicit clique constraints is that Σ_(j:S)_(j) _(εS) _(m) q_(j)≦1, i.e. only one set can be chosen in each clusterwhere S_(m) captures all the cooperating sets in cluster m. Theconstraint to the optimization problem is that the net accessprobability in each maximal clique should be bounded by one to avoidcollision. The resulting rate of each CP would be a function of all thecooperating sets it belongs to as well as their respective collisionprobability. The output of this optimization problem directly providesthe channel-access probability for each cooperating set. While theoptimization involves knowledge of the maximal cliques and collisionprobabilities, which can be obtained based on interference measurementsobtained and communicated by receivers 106, it will now be shown thatthe problem can be solved in a completely decentralized manner.

Observing that NCA is a concave optimization problem, we can obtain itsLagrangian as:

$L = {{\sum\limits_{C_{m} \in C}{\sum\limits_{i \in C_{m}}{\alpha_{m}{U\left( r_{i} \right)}}}} - {\sum\limits_{j}{\sum\limits_{k:{S_{j} \in M_{k}}}{\beta_{k}{q_{j}.}}}}}$

We can write β_(k) as β_(k)=β_(p) _(k) , where p_(k) is the collisionprobability in maximal clique M_(k). Since a cooperating set can belongto multiple maximal cliques, we have P_(j)=1−Π. If the collision in eachclique is kept small, then we can approximate P_(j)≈Σ_(k:S) _(j) _(εM)_(k) p_(k). Thus, to obtain P_(j), it is sufficient for each cooperatingset to track its net loss probability, which is a function of the sum ofthe loss probabilities in its constituent cliques. Thus, substitutingfor β_(k) and applying the approximation, we have

$L = {\sum\limits_{m:{C_{m} \in C}}L_{m}}$${where},{L_{m} = {{\sum\limits_{i \in C_{m}}{\alpha_{m}{U\left( r_{i} \right)}}} - {\beta{\sum\limits_{{{j:{({i \in S_{j}})}}\&}{({i \in C_{m}})}}{P_{j}q_{j}}}}}}$

A game-theoretic analysis has been used to show that the aggregateutility for a system of equations of the above form is maximized whenthe individual components maximize their own utility. While thecomponent corresponds to a single link in omnidirectional communication,the component corresponds to a cluster in network MIMO. Thus, jointadaptation of cooperating sets within each cluster should be used toobtain optimality.

Applying the Karush-Kuhn-Tucker (KKT) conditions for optimality withrespect to each cooperating set, we obtain

$\begin{matrix}{{\frac{\mathbb{d}L_{m}}{\mathbb{d}q_{j}} = {{{\sum\limits_{i \in S_{j}}{\alpha_{m}{U^{\prime}\left( r_{i} \right)}\left( {1 - P_{j}} \right)}} - {\beta\; P_{j}}} = 0}},{\forall{S_{j} \in S_{m}}}} & (1)\end{matrix}$With our desired fairness model being proportional fairness, we employ alogarithmic function of the rate for our utility functions(U(r_(i))=log(r_(i))). Thus, at the optimal solution, for everycooperating set we have

$\frac{\mathbb{d}L_{m}}{\mathbb{d}q_{j}} = {{{\sum\limits_{i \in S_{j}}\frac{\alpha_{m}\left( {1 - P_{j}} \right)}{r(i)}} - {\beta\; P_{j}}} = 0}$$\frac{\mathbb{d}L_{m}}{\mathbb{d}q_{j}} = {{{\alpha_{m}\left( {1 - P_{j}} \right)} - {\beta\;{P_{j}\left( \frac{1}{\sum\limits_{i \in S_{j}}\left( \frac{1}{r_{i}} \right)} \right)}}} = 0}$

While α_(m) can be used to prioritize different clusters, for an equalbias, we set α_(m)=α in one embodiment. Given that P_(j) can be locallyinferred by each cooperating set S_(j), the above optimality conditioncan be used to adapt its access probability in a completelydecentralized manner as follows:

$\left. q_{j}\leftarrow{q_{j} + {\alpha\left( {1 - P_{j}} \right)} - {\beta\;{{P_{j}\left( \frac{1}{\sum\limits_{i \in S_{j}}\left( \frac{1}{r_{i}} \right)} \right)}.}}} \right.$Such an adaptation has the following important properties.

Firstly, adaptation is performed jointly within a cluster. Unlikeomnidirectional communication, where adaptations are purelylink-specific, in exemplary embodiments of the present principles, theaccess adaptation for a cooperating set is coupled with the rate of theconstituent CPs, which, in turn, is a function of all the cooperatingsets that each CP belongs to—in essence, the access adaptation of acooperating set should be performed jointly with that of other sets inthe cluster to achieve optimality. In particular, the collision betweencooperating sets within the same cluster can be resolved internally bythe AP, instead of wasting the transmission attempt in packet collision.

Secondly, each AP converges to the solution of NCA. Although coupled,the adaptation mechanism for each cooperating set follows a gradientapproach based on the KKT conditions. Further, since the utilityfunction

U(r_(i))=log(r_(i))=log(Σ_(j:iεA) _(j) (1−P_(j))q_(j)) and the resultingLagrangian are concave with respect to each q_(j), there exists a uniquemaximum, to which the individual adaptations can be shown to converge.

Thirdly, the scheme can be practically realized through a distributedantenna system (DAS). An important aspect in realizing optimalitythrough the above-described adaptation is the joint adaptation of accessparameters within a cluster. This in turn indicates that the collision,access and rate parameters of all cooperating sets should be shared andcoordinated among each CP. However, such a MAC requirement (in additionto tight phase synchronization) is difficult to realize in practice,where distributed APs are coordinated on an ethernet backhaul to achievenetwork MIMO. On the other hand, by virtue of enabling cooperationbetween CPs at the physical layer, DAS not only permits for easiersynchronization but also for coordination and joint adaptation of accessparameters between cooperating sets.

With reference now to FIG. 3, a diagram of an exemplary method 300 forchannel access adaptation in accordance with an exemplary embodiment isillustratively depicted. The method 300 can be performed by each AP 102of each cluster. Further, each AP 102 can perform the method 300independently from APs of other clusters. The method can begin at step302, at which the controller 204 of the AP 102 can receive channel stateinformation, including interference measures, through the CPs 104 in itscluster from the receivers 106 served in its cluster. At step 304, thecontroller 204 and the channel contention module 214 can determinechannel allocations for the remote antennas of the cluster. For example,the channel state information can be employed by the controller 204 todetermine the interference cliques, for example the maximal cliquesM_(k), and the collision loss probabilities P_(j) for corresponding setsS_(j) 112 of cooperating remote antennas. As indicated above, eachinterference clique can denote a group of the sets S_(j) 112 ofcooperating remote antennas, where each set in the group either inflictsor undergoes interference from another set in the group that exceeds athreshold interference, which can be based on SINR. The maximal cliquecan be determined by searching all interference cliques (for exampledefined by a maximum SINR or a threshold SINR), finding the one whichhas most number of nodes included therein and selecting this clique withthe most number of nodes as the maximal clique. Alternatively, asuboptimal approach, for example, based on greedy search, can be used todetermine a maximal clique. As also indicated above, the controller 204can compute P_(j) for a set S_(j) 112 by tracking a net loss probabilityby determining a sum of the loss probabilities of the set in itsconstituent cliques, i.e. by summing each constituent collision lossprobability from a different corresponding interference clique to whichthe set S_(j) 112 belongs. The collision loss probability may bedetermined for example by time averaging the number of collisions.Accordingly, the controller 204, along, with the channel contentionmodule 214, as discussed below, can adapt channel allocations to theremote antennas 104 of the cluster of its AP based on a tracking of thesums of collision loss probabilities of the sets S_(j) 112, where eachsum is determined for a different given set S_(j) in the cluster.Thereafter, the controller 204 and the channel contention module 214 candetermine a corresponding channel access probability q_(j) for each ofthe sets of cooperating remote antennas in the cluster of the accesspoint based on P_(j) for purposes of performing or adapting channel orchannel resource allocation.

For example, referring now to FIG. 4, with continuing reference to FIG.3, the cluster AP 102, at step 304, runs the adaptation for each of itscooperating sets. FIG. 4 provides a state diagram 400 for a given setS_(j) 112 of cooperating antennas 104 illustrating the adaptation forthe set. The channel contention module 214 can select one of the sets112 of cooperating remote antennas in the cluster to transmit data, on achannel resource, to one or more receivers 106 that is served by the setbased on updates of the channel access probabilities of the sets 112 inthe cluster. For example, a given set can be in one of three states:Idle 402, Contend 404 and Acquire 406. When all CPs in a set S_(j)senses an idle channel or channel resource for a short duration (forexample, the DIFS time defined in 802.11), S_(j) enters the idle state402 for the channel resource. Here, along line 408, the channelcontention module 214 determines whether ε_(j)>q_(j), where ε_(j) is arandom number between 0 and 1. If not, then channel contention module214 updates q_(j) by incrementing it as follows: q_(j)+=α. In addition,the determination of whether ε_(j)>q_(j) is repeated. If ε_(j)>q_(j),then the channel contention module 214 directs the CPs 104 of the setS_(j) to make a contention attempt with probability q_(j) along line 410and to enter the contention state 404. Here, the contention attempt is adesignation of the set S_(j) as being in the contention state; the setS_(j) need not attempt to access a channel resource in the contentionstate. Since Σ_(j:S) _(j) _(εS) _(m) q_(j≦)1 in each cluster, thechannel contention module 214 picks a local winning set with a highestprobability q_(j). The winning set then, along line 414, enables netMIMOtransmission among all its CPs. Here, the channel contention module 214updates q_(i) by incrementing q_(j): q_(j)=α and the winning set entersthe acquire state 406, where it acquires the channel resource(s) toperform the netMIMO transmission. Thereafter, the channel contentionmodule 214 directs the winning set to enter the idle state 402 alongline 416. While the contention (loss) of a winning set with sets inother clusters has to be inferred through packet collisions, this is notrequired for local contention loss between sets in the same cluster thatcan be inferred directly by the AP. The transmission operation can alsobe preceded by a random backoff window (0, B), where B is a constant(window size), to increase efficiency by reducing the impact ofcollisions. If there is a contention loss (happens with probabilityP_(j)q_(j)), the channel contention module 214 decrements the accessprobability by

${\frac{\beta\;}{q_{j}}\left( \frac{1}{\sum\limits_{i \in S_{j}}\left( \frac{1}{r_{i}} \right)} \right)} - \alpha$while it increments the access probability by α on a successfultransmission. Thus, returning to state 404, if any set S_(j) does notwin the contention, then the channel contention module 214 directs thatset S_(j) to enter the idle state 402 along line 412 and updates itsprobability q_(j):

$q_{j}+={\alpha - {\frac{1}{q_{j}}{\left( {{\beta\left( {\sum\limits_{i \in S_{j}}\frac{1}{r_{j}}} \right)}^{- 1} + \alpha} \right).}}}$While there are multiple ways to realize

$\left. q_{j}\leftarrow{q_{j} + {\alpha\left( {1 - P_{j}} \right)} - {\beta\;{P_{j}\left( \frac{1}{\sum\limits_{i \in S_{j}}\left( \frac{1}{r_{i}} \right)} \right)}}} \right.$by distributing the adaptation between different states, the preferredembodiment realizes it as in FIG. 4, where the increment of q_(j) in theidle mode was found to contribute to faster convergence.

In each cluster, the number of cooperating sets increases exponentiallywith the number of CPs. However, not all cooperating sets contributeequally to the cluster's capacity. The pruning module 212 discriminatesthem by defining a dominance relation. A cooperating set S_(i) is saidto dominate Sj within the same cluster if S_(j)'s CPs is a subset ofS_(i)'s, and S_(i)'s interfering CPs in neighboring clusters is a subsetof Sj's. Assuming the carrier sensing relation between CPs to besymmetric, then equivalently, S_(i) should have similar accessprobability (and causes similar interference to competing sets) with Sjbut it has a higher multiplexing gain by enabling more concurrenttransmissions. Hence, the pruning module 212 eliminates the dominatedset S_(j) to achieve a higher efficiency. Each CP learns the set ofinterfering CPs in the cluster through carrier sensing, and the pruningmodule 212 of the cluster AP periodically checks the dominance relationbetween all cooperating sets, and then prunes the dominated ones. Whilethe distributed NUM framework would automatically discriminate suchdominated sets, it would still need to allow those sets to contend forchannel access (by increasing α) and then converge back to lowcontention probability, whose associated overhead is eliminated by thepruning process.

At step 306, as described herein below, the client selection module 216can perform client selection and the precoding and power allocationmodule 220 can jointly determine precoding and power control for one ormore sets Sj selected at step 304. At step 308, the controller 204 candirect the remote antennas 104 to transmit data to the receivers inaccordance with the channel allocations determined at step 304 and withthe client selection, precoding and power control determined at step306.

As indicated above, optimizing the scheme within each cluster at step306 involves two components: joint power control, client selection andprecoding for scalable netMIMO performance; and CP suppression forcreating reuse opportunities across clusters. The first component isencountered after a cooperating set wins the access opportunity. Here,the AP employs an algorithm to assign the CPs in the set to clients withdownlink packets. The assignment problem involves several coupledfactors: (i) The number of clients should not exceed the number of CPsin the cooperating set; (ii) A client's achievable rate depends on notjust its own channel gain, but also peers in the same set; (iii) Thepower budget of each CP should be spent intelligently, such thatsufficient “cancellation power” is used to eliminate the inter-streaminterference, and the remaining power ensures efficient and fair rateallocation among clients. The system can meet these specifications byjointly allocating CP power, designing the precoding matrix, andselecting the set of downlink clients. First, the power allocation andprecoding problem for the case when the number of clients are alreadyselected is considered. Then how client selection can be incorporatedjointly will be described.

When the number of clients is no more than the number of CPs, oneapproach for MU-MIMO down-link transmission is zero-forcing beamforming,i.e., using the pseudo-inverse of the channel matrix directly as theprecoding matrix: v=h′(hh′)⁻¹. Suppose v_(ki) are elements of theprecoding matrix v, denoting the precoding weight of the k-th CP fori-th client (stream). Since hv=l (unity matrix), each client onlyreceives the data intended for itself, while other streams are nullifiedafter precoding and channel distortion.

In CAS (Co-located Antenna Systems), antenna power allocation can beseparated from the design of the precoding matrix. A practical approachallocates an equal amount of power for all clients (streams) bynormalizing each CP's power budget with the maximum magnitude of theprecoding vectors of all CPs:

$A_{i}^{2} = {{\max\;}_{j \in S}{\frac{P}{\overset{D}{\sum\limits_{k = 1}}{v_{kj}}}.}}$This approach has been shown to be close to optimal in practical CAS butcan significantly degrade the performance in a DAS set-up, especiallywhen the topology is imbalanced. Hence, the preferred embodiment adoptsa rigorous formulation for jointly designing the precoding matrix andpower allocation to maximize the aggregate achievable rate, whileensuring proportional fairness among clients.

The power that a client or receiver 106 receives depends on the elementsof the precoding matrix, i.e., how each CP weights the data symbols ofeach client. The problem can be formulated as:

$\begin{matrix}{\max{\overset{D}{\sum\limits_{i = 1}}{w_{i}{\log\left( {1 + \frac{P_{i}}{N_{0}}} \right)}}}} & (2) \\{{{s.t.\mspace{14mu} P_{i}} = {{\overset{S}{\sum\limits_{k = 1}}{h_{ik}v_{ki}}}}^{2}},{\forall{i \in D}}} & (3) \\{{{\overset{D}{\sum\limits_{k = 1}}{v_{ki}}^{2}} \leq P},{\forall{k \in S}}} & (4) \\{{{\overset{S}{\sum\limits_{k = 1}}{h_{jk}v_{ki}}} = 0},{i \in D},{j \neq i}} & (5)\end{matrix}$where D and S are the set of clients and CPs, respectively, and h_(ik)the complex channel gain from CP k to client i. Eq. (4) is theper-antenna power constraint. Eq. (5) represents the precodingconstraint, i.e., precoded symbols intended for client i should canceleach other when arriving at client j after channel distortion (i≠j).From Eq. (5), it can be seen that when the estimated channel matrix fora client j becomes outdated (e.g., due to movement), the interferencefrom another stream may become non-trivial, but the change only affectsclient j. Therefore, the channel estimation can be on-demand—the CPs mayrequest a client to feedback its channel only if it has a low receptionratio.

In the above formulation, w_(i) is the weight allocated to each client,and can be adjusted to achieve a certain long-term fairness objective.For proportional fairness, we can configure w_(i) to be the inverse ofthe time-averaged throughput of i, i.e., for each slot of transmissionattempt t, suppose client i achieves a rate of R_(i), then we update thethroughput and weight as:

$\begin{matrix}{R_{{it}\leftarrow{\gamma\; R_{i{({t - 1})}}}} + {\left( {1 - \gamma} \right)R_{i}}} & (6) \\\left. w_{i}\leftarrow\frac{1}{R_{it}} \right. & (7)\end{matrix}$where γ is a smoothing factor and 0<γ<1. The objective function hasproven to be non-convex with respect to the real and imaginarycomponents of the precoding weights v_(ki) due to the norm operator inEq. (3). Fortunately, by phase-shifting the vector v_(ik), ∀kεSappropriately, we can restrict Im(Σ_(k=1) ^(|S|)h_(ik)v_(ik))=0, whileboth constraints (4) and (5) are invariant to the phase-shift. Theresulting problem then becomes convex as long as P_(i)>N₀, and can beeasily solved using standard convex optimization techniques.

When the number of clients |D| is larger than the number of CPs |S|, weshould select an appropriate set of clients (maximum size |S|) so as toachieve the desired efficiency and fairness objective. A straightforwardapproach is to augment an indicator variable to the above optimizationproblem to select the optimal set of clients for each transmissionattempt. However, this makes the problem intractable due to the integerand non-linear constraints. Therefore, an alternate, iterative approachfor the joint design of client selection, precoding and power allocationis employed: i) First, a client that has the highest utility (resultingfrom the above optimization) when all available CPs are beamforming toit is elected; ii) Second, another client that results in the highestutility when grouped with the previously selected clients is selected;and iii) If the utility is lower than the previous round, or if thereare no clients to be selected, stop. Otherwise, step ii) is performed.

In a DAS, the clients and CPs tend to spread over a large area, andsmall-scale fading (which causes small magnitude and phase variation)has little impact on the MU-MIMO capacity. Therefore, the preferredembodiment runs client selection using the channel gains in the latesttransmission attempt, and then estimates the channel from available CPsto the selected set of clients to perform precoding, in order to reducethe channel estimation overhead.

The client selection can involve multiple rounds of non-linearoptimization, which is inefficient when the client population is large.The bulk of the complexity is contributed by the precoding and powerallocation process. Thus, a hybrid algorithm that balances the tradeoffbetween computational complexity and performance can be employed. It canbe shown that the simple equal-power allocation suffers from severeperformance degradation in an imbalanced topology. Thus, the topologyimbalance is characterized using a ρ-factor. For a given set of CPs, theρ-factor is the fairness index with respect to the number of times a CPis preferred in the set. Each client ranks the CPs according to thechannel gain it sees from them, and a CP is preferred once if it is thetop-rank of one of the clients. Intuitively, the ρ-factor reflects theimbalance of the topology. It is close to 0 when all clients areconcentrated near one CP or when there are fewer number of clients thanCPs, and close to 1 when they are evenly distributed among CPs (which ismore likely to happen when the client population is large). Thus, thepreferred embodiment adopts a hybrid power allocation scheme to strike abalance between performance and complexity, using the ρ-factor as adecision variable. When ρ is below a threshold ρ₀ (we use an empiricalvalue of 0.5), the system can employ the simple equal power allocationscheme during the client selection process, while restricting the use ofthe optimized power allocation scheme to highly imbalanced topologies.This permits the system to reduce complexity significantly withoutsacrificing in performance appreciably.

As part of the second component discussed above, CP suppression, theembodiments described herein strive to strike a balance betweencooperation gain within a cluster and spatial reuse between clusters.The CP suppression module 210 of each AP in each cluster can beconfigured to opportunistically suppress certain CPs that: (i)contribute mainly to the diversity gain in the cluster; (ii) experiencesufficient contention with adjacent clusters and (iii) causeinterference to neighboring clusters' clients with downlink packets.

To evaluate the first condition, the CP suppression module 210 runs asimple stable matching algorithm. Specifically, it assigns to eachclient (with downlink traffic) a ranked list of “preferred CPs” based onthe channel gain matrix between the CPs and the client (CPs that cannotreach the client are excluded), and to each CP a ranked list of“preferred clients.” It then runs the classical stable marriage problembetween the CPs and clients. CPs not in the matched set are consideredto contribute little to the multiplexing gain, and will be suppressedwhen spatial reuse can be exploited.

To evaluate the second condition, each CP i locally computes acontention factor (F_(i)) that reflects the intensity of contentionbetween each CP and other CPs in adjacent clusters. This is in turndefined as:

$\begin{matrix}{F_{i} = {\sum\limits_{j:{i \in S_{j}}}\left( {P_{j} - P_{j}^{\prime}} \right)}} & (8)\end{matrix}$where C(i) is the cluster that i belongs to. For each cooperating setS_(j), P_(j) and P′_(j) denote the net collision probability andcollision probability due to other sets in the same clusterrespectively. Hence, F_(i) captures the desired inter-cluster collisionprobability experienced by CP i. A large F_(i) value indicates highercontention, and hence a higher potential for spatial reuse to beeffectively exploited between clusters.

To evaluate the third condition, a CP estimates whether clients inneighboring clusters have traffic demand by over-hearing the clear tosend (CTS) packets from them. The CTS scheme used here is described inmore detail herein below. If so, and if a CP i is not matched whenevaluating the first condition, and if F_(i) is larger than a thresholdφ_(i) (we use an empirical value of 0.8), CP i would refrain fromleveraging diversity gain, and leave the opportunity for clients inneighboring clusters to leverage spatial reuse instead.

To obtain the channel matrix from CPs in a cooperating set for selectedclients, the 802.11ac channel estimation scheme can be used. However,the AP integrates it with a request to send (RTS)/CTS exchange that alsodoubles up to reduce hidden terminals. Before the transmission attemptof a cooperating set, all CPs within it synchronously broadcast an RTSpacket that indicates the duration of transmission and addresses ofselected clients. Then, the selected clients return a CTS packet. Theheader part of the CTS packet includes the transmission duration, whichis sent by all clients synchronously to reserve a channel fromtransmitters in neighboring clusters. The second part is sentsequentially by each client (the order follows the order of addresses inthe RTS), and includes the channel gain information from the CPs toclients. After completing the data transmission, an acknowledgement(ACK) is also sent sequentially by each client in a similar manner.While such an RTS/CTS/ACK scheme incurs overhead, employing it for thechannel estimation procedure (important for netMIMO), amortizes itsoverhead. In addition, it alleviates hidden terminals, whose impact onnetMIMO is more pronounced compared to single-input and single-output(SISO) communication. Further, the overhead is negligible in comparisonwith the netMIMO gains.

Block 213 of FIG. 2 summarizes the system's flow of operationsthroughout one transmission attempt. The AP periodically runs the CPsuppression algorithm to exclude certain CPs from channel contention topromote reuse across clusters. The AP continuously contends for channelaccess, for example, by running the distributed channel access algorithmdescribed above with respect to FIG. 4, on behalf of all the cooperatingsets consisting of the remaining CPs (after the pruning operation). Thecooperating set that wins contention in each cluster will start thetransmission attempt immediately. It first uses the client selectionalgorithm described above to determine the set of clients to servejointly through netMIMO. Then, the CPs in the winning set initiate theRTS/CTS exchange with the selected clients, estimate the correspondingchannel matrix as discussed above, and the AP computes the precodingmatrix along with power allocation as discussed above. It then executesthe netMIMO operation with data transmission, followed by ACK receptionfrom the clients—the latter being used to infer transmission success andupdate contention parameters.

As also illustrated in FIG. 2, in the transmit path, after theclient-selection algorithm is run to determine the set of CPs/clients tobe used for netMIMO transmissions, the digital bits of each client aremapped to symbols via, for example, binary phase shift keying (BPSK).NEMOx's joint precoding and power-allocation algorithm is then run andis performed in the frequency domain (on the symbols carried by eachOFDM subcarrier). The precoded symbols for each CP are then modulatedusing OFDM and sent over the air.

Each client runs the receiver path that detects the packets and thenestimates the channel from each CP. A packet follows a similar formatwith 802.11ac. It starts with a short-preamble used forself-correlation-based packet detection. Then a sequence of longpreambles is sent consecutively by each CP. The long preambles are usedfor estimating the frequency offset between the transmitter and thereceiver, as well as the channel phase/amplitude distortion to each OFDMsubcarrier. These channel estimation results are fed back to the AP toperform the intra-cluster optimization of step 306. Following thelong-preambles is an additional preamble sent by all CPs concurrentlyand used to estimate the composite channel created by channel distortionand precoding. Based on the estimation results, the receiver demodulatesthe OFDM symbols and decodes the digital bits therein. Within each OFDMsymbol, four pilot subcarriers (with known bits), for example, can besent by the transmitter and used for correcting residual errors in theCFO estimation using long-preambles.

Having described preferred embodiments of scalable network MIMO methodsand systems (which are intended to be illustrative and not limiting), itis noted that modifications and variations can be made by personsskilled in the art in light of the above teachings. It is therefore tobe understood that changes may be made in the particular embodimentsdisclosed which are within the scope of the invention as outlined by theappended claims. Having thus described aspects of the invention, withthe details and particularity required by the patent laws, what isclaimed and desired protected by Letters Patent is set forth in theappended claims.

What is claimed is:
 1. A system for channel access adaptation, thesystem comprising: a plurality of remote antennas configured to transmitdata to receivers and to obtain channel state information; and aplurality of access points, wherein each of the access points isconfigured to control a different cluster of said remote antennas,receive a respective channel state information from the remote antennasof a respective cluster and, independently from other access points ofsaid plurality of access points, adapt channel allocations to the remoteantennas of the respective cluster based on a tracking of sums ofcollision loss probabilities, wherein each given sum of the sums isdetermined by a corresponding access point for a different given set ofa plurality of sets of cooperating remote antennas in the respectivecluster and wherein each constituent collision loss probability in agiven sum is determined by the corresponding access point from adifferent corresponding interference clique to which the different givenset belongs.
 2. The system of claim 1, wherein each interference cliquedenotes a respective group of the sets of cooperating remote antennasand wherein each set in the group either inflicts or undergoesinterference from another set in the group that exceeds a thresholdinterference.
 3. The system of claim 2, wherein each of the accesspoints is configured to determine the respective collision lossprobabilities and respective interference cliques based on measures ofsaid interference that are included in said respective channel stateinformation.
 4. The system of claim 1, wherein each of the access pointsis configured to determine channel access probabilities for the sets ofcooperating remote antennas in the cluster of the respective accesspoint based on the respective sums of collision loss probabilities. 5.The system of claim 4, wherein each of the access points is configuredto select one of the sets of cooperating remote antennas in the clusterof the respective access point to transmit a portion of said data to atleast one of the receivers that is served by the selected set ofcooperating remote antennas on a channel resource based on updates ofthe channel access probabilities of the sets of cooperating remoteantennas in the cluster of the respective access point.
 6. The system ofclaim 5, wherein said update for a corresponding set of the sets ofcooperating remote antennas is based on a contention scheme between thesets of cooperating remote antennas.
 7. The system of claim 6, whereinthe cooperating remote antennas in the selected set transmit the samedata simultaneously and wherein the respective access point isconfigured to jointly determine power allocation and precoding for theselected set.
 8. A system for channel access adaptation, the systemcomprising: a cluster of remote antennas configured to transmit data toreceivers and to obtain channel state information; and an access pointconfigured to receive the channel state information from the remoteantennas and adapt channel allocations to the remote antennas based on atracking of sums of collision loss probabilities, wherein each given sumof the sums is determined by the access point for a different given setof a plurality of sets of cooperating remote antennas in the cluster andwherein each constituent collision loss probability in a given sum isdetermined by the access point from a different correspondinginterference clique to which the different given set belongs.
 9. Thesystem of claim 8, wherein each interference clique denotes a respectivegroup of the sets of cooperating remote antennas and wherein each set inthe group either inflicts or undergoes interference from another set inthe group that exceeds a threshold interference.
 10. The system of claim9, wherein the access point is configured to determine the collisionloss probabilities and interference cliques based on measures of saidinterference that are included in said channel state information. 11.The system of claim 10, wherein the access point is configured todetermine channel access probabilities for the sets of cooperatingremote antennas in the cluster based on the sums of collision lossprobabilities and wherein the access point is configured to select oneof the sets of cooperating remote antennas in the cluster to transmit aportion of said data to at least one of the receivers that is served bythe selected set of cooperating remote antennas on a channel resourcebased on updates of the channel access probabilities of the sets ofcooperating remote antennas in the cluster.
 12. The system of claim 11,wherein said update for a corresponding set of the sets of cooperatingremote antennas is based on a contention scheme between the sets ofcooperating remote antennas.
 13. The system of claim 12, wherein thecooperating remote antennas in the selected set transmit the same datasimultaneously and wherein the access point is configured to jointlydetermine power allocation and precoding for the selected set.
 14. Amethod for channel access adaptation, the method comprising: receivingchannel state information from a cluster of remote antennas; determiningchannel allocations for the remote antennas of the cluster based on atracking of sums of collision loss probabilities, wherein each given sumof the sums is determined for a different given set of a plurality ofsets of cooperating remote antennas in the cluster and wherein eachconstituent collision loss probability in a given sum is determined froma different corresponding interference clique to which the differentgiven set belongs; and transmitting data from at least one of said setsof cooperating remote antennas in the cluster in accordance with thechannel allocations.
 15. The method of claim 14, wherein eachinterference clique denotes a respective group of the sets ofcooperating remote antennas and wherein each set in the group eitherinflicts or undergoes interference from another set in the group thatexceeds a threshold interference.
 16. The method of claim 15, whereinthe determining the channel allocations further comprises determiningthe collision loss probabilities and interference cliques based onmeasures of said interference that are included in said channel stateinformation.
 17. The method of claim 14, wherein the determining thechannel allocations further comprises determining channel accessprobabilities for the sets of cooperating remote antennas in the clusterbased on the sums of collision loss probabilities.
 18. The method ofclaim 17, wherein the determining the channel allocations furthercomprises selecting one of the sets of cooperating remote antennas inthe cluster to transmit a portion of said data to at least one receiverthat is served by the selected set of cooperating remote antennas on achannel resource based on updates of the channel access probabilities ofthe sets of cooperating remote antennas in the cluster.
 19. The methodof claim 18, wherein said update for a corresponding set of the sets ofcooperating remote antennas is based on a contention scheme between thesets of cooperating remote antennas.
 20. The method of claim 19, whereinthe cooperating remote antennas in the selected set transmit the samedata simultaneously and wherein the method further comprises: jointlydetermining power allocation and precoding for the selected set.